Recent advances in language modeling have focused on (potentially multiple-choice) question answering, open-ended generation, or math and coding problems. We look at a more nuanced task: the interpretation of statements of political actors. To this end, we present a dataset of policy announcements and corresponding annotated interpretations, on the topic of US foreign policy relations with Russia in the years 1993 up to 2016. We analyze the performance of finetuning standard sequence-to-sequence models of varying sizes on predicting the annotated interpretations and compare them to few-shot prompted large language models. We find that 1) model size is not the main factor for success on this task, 2) finetuning smaller models provides both quantitatively and qualitatively superior results to in-context learning with large language models, but 3) large language models pick up the annotation format and approximate the category distribution with just a few in-context examples.

A wide variety of tasks have been framed as text-to-text tasks to allow processing by sequence-to-sequence models. We propose a new task of generating a semi-structured interpretation of a source document. The interpretation is semi-structured in that it contains mandatory and optional fields with free-text information. This structure is surfaced by human annotations, which we standardize and convert to text format. We then propose an evaluation technique that is generally applicable to any such semi-structured annotation, called equivalence classes evaluation. The evaluation technique is efficient and scalable; it creates a large number of evaluation instances from a comparably cheap clustering of the free-text information by domain experts. For our task, we release a dataset about the monetary policy of the Federal Reserve. On this corpus, our evaluation shows larger differences between pretrained models than standard text generation metrics.